Understanding Plant Water Status: A Deeper Dive into VPD and Canopy Temperature

After my recent post about the Hidden Complexities of VPD Measurements, and reading the comments, I realized I needed to provide more information on this topic.

Let's assume we can obtain accurate canopy surface temperature data, target plant canopies are healthy (not affected by disease or pests), measurement timing is right, and we're dealing with a plant species that responds to VPD changes.

Here are two key questions to consider:

  1. How should canopy surface temperature readings look like in a well-watered plant versus a water-stressed plant? 

  2. How can we take advantage of canopy temperature and VPD measurements? 

Canopy-to-air temperature difference (ΔT = Tc - Ta) is fundamental. For most plant species responding to VPD, ΔT should be negative, signifying active transpiration.

When plants close stomata due to water stress (soil moisture deficit), transpiration diminishes, and leaf surface temperature rises. Transpiration has a cooling effect on the leaf, similar to sweat on human skin.

Even with sufficient soil moisture, high atmospheric demand (driven by high wind, solar radiation, temperature, and low RH - high VPD) can induce stomatal closure to prevent excessive water loss.


Figure 1. We can use a thermal gun to measure the canopy surface temperature.

Even with sufficient soil moisture, high atmospheric demand (driven by high wind, solar radiation, temperature, and low RH - high VPD) can induce stomatal closure to prevent excessive water loss.

High transpiration rates, driven by these factors, lead to more negative ΔT values. Conversely, low wind, radiation, temperature, and high relative humidity (low VPD) result in ΔT values closer to or even exceeding zero.

The plant's response to these environmental factors can be complex, especially when their influences on transpiration are not unidirectional. This necessitates the use of models (empirical or biophysical) to mathematically describe these intricate relationships.

Existing evapotranspiration (ET) models like Penman-Monteith lack canopy surface temperature as an input. Therefore, a transpiration model incorporating both environmental parameters and plant feedback (i.e. canopy surface temperature) is highly recommended for accurate water loss estimation.

We can also utilize a valuable index: the Crop Water Stress Index (CWSI). CWSI, available in both empirical and theoretical forms, helps determine plant water stress. Simply relying on measured ΔT and VPD values is insufficient for decision-making.

We can also utilize a valuable index: the Crop Water Stress Index (CWSI). CWSI, available in both empirical and theoretical forms, helps determine plant water stress. Simply relying on measured ΔT and VPD values is insufficient for decision-making.

We need to establish wet (non-stressed) and dry (stressed) ΔT thresholds for our plants and compare measured values against these thresholds. CWSI facilitates this normalization:

CWSI = (ΔTm - ΔTl) / (ΔTu - ΔTl)

where:

  • ΔTl: Temperature difference under non-limiting soil water availability (well-watered plant canopy)

  • ΔTu: Temperature difference for a non-transpiring canopy (dead)

  • ΔTm: Difference between measured canopy (Tc) and air (Ta) temperatures


Figure 2. Crop Water Stress Index (CWSI). In a non-stressed plant: A = 0, CWSI = 0, and in a severely stressed plant: A = B, CWSI = 1.

CWSI values range from 0 to 1. Values closer to zero indicate well-watered plants, while values closer to 1 signify highly stressed plants. By establishing CWSI thresholds based on the specific water requirements of our plants, we can effectively use this index for irrigation scheduling.

For a deeper dive into CWSI, I recommend this paper:

Daylight crop water stress index for continuous monitoring of water status 

Comments

Post a Comment

Popular posts from this blog

Leveraging Microclimate Data, Proximal Canopy Sensing, and Machine Learning for Precise Soil Water Potential Prediction

Image
  Introduction Accurately predicting soil water potential is crucial for optimizing irrigation practices and minimizing water waste in agriculture. This project delves into the critical relationship between high resolution, in-field microclimate measurements, canopy surface temperature , time of day, and soil water content and potential. Building upon our previous work in biophysical modeling , we employ advanced machine learning techniques to investigate the impact of measurement timing on prediction accuracy. Enhanced Prediction with Machine Learning This project utilizes a range of machine learning algorithms, including: Linear Regression:  Establishes a linear relationship between features and target variables. Decision Tree Regressor:  Makes decisions based on a series of if-else questions. Random Forest Regressor:  An ensemble method combining multiple decision trees for improved accuracy and reduced overfitting. Gradient Boosting Regressor:  Iteratively b...
Read more

The Rise of the "Human Data Farms": A Satirical Look at the Future of AI

Image
  The race for Artificial General Intelligence (AGI) has reached a fever pitch, with tech giants vying for dominance in the AI landscape. As Elon Musk has pointed out ( https://www.theguardian.com/technology/2025/jan/09/elon-musk-data-ai-training-artificial-intelligence ), the increasing scarcity of data for training AI models is a significant challenge. But as data becomes increasingly scarce, a new and perhaps more sinister solution is emerging: the rise of the "Data Farms." Imagine a dystopian future where tech companies, desperate for the raw materials needed to fuel their AI models, begin constructing self-contained communities. These "Human Data Farms" would resemble a hyper-surveilled version of The Truman Show, where residents are paid handsomely to live their lives under constant observation. Every interaction, every conversation, every emotion – all meticulously recorded and fed into the insatiable maw of AI algorithms. This scenario, while seemingly absur...
Read more

For CEA Growers: Tips for Substrate Monitoring and Automation

Image
  Based on my communications and meetings with our customers on solutions for substrate monitoring and decision making in controlled environment agriculture (CEA), I have identified a numbers of important areas to briefly discuss:   1) Sensor quantity optimization Most growers do not need a lot of sensors to monitor their grow rooms. There are good ways of determining the optimum number of sensors required for different applications. In addition to an unnecessarily high cost, a large number of sensors in one facility can create serious design, installation and maintenance headaches for different parties involved. More data does not translate into better insight or decision-making unless we know how to deal with “big data”. Large amount of data that grower does not know what to do with leads to frustration and mistrust in the technology.   More data does not translate into better insight or decisions unless we know how to deal with “big data”. Large amount of data that gro...
Read more